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IEEE Computational Intelligence Magazine

Issue 4 • Nov. 2016

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Displaying Results 1 - 23 of 23
  • [Front cover]

    Publication Year: 2016, Page(s): C1
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  • IEEE Transactions on Emerging Topics in Computational Intelligence

    Publication Year: 2016, Page(s): C2
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  • Table of contents

    Publication Year: 2016, Page(s): 1
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  • CIM Editorial Board

    Publication Year: 2016, Page(s): 2
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  • IEEE Standards [Editor's Remarks]

    Publication Year: 2016, Page(s): 2
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  • CIS Society Officers

    Publication Year: 2016, Page(s): 3
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  • Celebration of Light [President's Message]

    Publication Year: 2016, Page(s):3 - 13
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  • IEEE 1855™: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]

    Publication Year: 2016, Page(s):4 - 6
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  • IEEE CIS VP-Technical Activities Vision Statement [Society Briefs]

    Publication Year: 2016, Page(s):6 - 7
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  • CIS Publication Spotlight [Publication Spotlight]

    Publication Year: 2016, Page(s):8 - 10
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  • IJCNN 2017

    Publication Year: 2016, Page(s): 11
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  • Model Complexity, Regularization, and Sparsity [Guest Editorial]

    Publication Year: 2016, Page(s):12 - 13
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  • Call for Papers for Journal Special Issues

    Publication Year: 2016, Page(s): 13
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  • Learning Distributional Parameters for Adaptive Bayesian Sparse Signal Recovery

    Publication Year: 2016, Page(s):14 - 23
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1982 KB) | HTML iconHTML

    Power Exponential Scale Mixture (PESM), a generalized scale mixture family of distributions, has been recently proposed to model the sparsity inducing prior distributions currently in use for Sparse Signal Recovery (SSR). In this paper, we review this generalized scale mixture family and establish the necessary and sufficient condition for a distribution (symmetric with respect to origin) to have ... View full abstract»

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  • Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection

    Publication Year: 2016, Page(s):24 - 35
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3230 KB) | HTML iconHTML

    Multivariate Analysis (MVA) comprises a collection of tools that play a fundamental role in statistical data analysis. These techniques have become increasingly popular since the proposal of Principal Component Analysis (PCA) in 1901 [1]. PCA was proposed as a simple and efficient way to reduce data dimension by projecting the data over the largest variance directions. As illustrated in Fig. 1, PC... View full abstract»

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  • Super-Sparse Learning in Similarity Spaces

    Publication Year: 2016, Page(s):36 - 45
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (4152 KB) | HTML iconHTML

    In a growing number of applications, including computer vision, biometrics, text categorization and information retrieval, samples are often represented more naturally in terms of similarities between each other, rather than in an explicit feature vector space [1], [2]. Traditional machine-learning algorithms can still be used to learn over similarity-based representations; e.g., linear classifica... View full abstract»

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  • Smart Car [Application Notes]

    Publication Year: 2016, Page(s):46 - 58
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (3610 KB) | HTML iconHTML

    In contrast to a traditional mechanical car, the Smart Car is a highly computerized automobile featuring ubiquitous computing, intuitive human-computer interaction and an open application platform. In this paper, we propose an advanced Smart Car demonstration platform with a transparent windshield display and various motion sensors where drivers can manipulate a variety of car-appropriate applicat... View full abstract»

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  • Distributed Reservoir Computing with Sparse Readouts [Research Frontier]

    Publication Year: 2016, Page(s):59 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (6142 KB) | HTML iconHTML

    In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development of distributed, message-passing implementations of the standard machine learning training algorithms. In this paper, we are concerned with the distri... View full abstract»

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  • [Conference Calendar]

    Publication Year: 2016, Page(s):69 - 70
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  • CEC 2017

    Publication Year: 2016, Page(s): 71
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  • 2016 Index IEEE Computational Intelligence Magazine Vol. 11 [Year-End Index]

    Publication Year: 2016, Page(s): 1
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  • FUZZ-IEEE 2017

    Publication Year: 2016, Page(s): C3
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  • DSAA2017 at TOKYO

    Publication Year: 2016, Page(s): C4
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Aims & Scope

The IEEE Computational Intelligence Magazine (CIM) publishes peer-reviewed articles that present emerging novel discoveries, important insights, or tutorial surveys in all areas of computational intelligence design and applications, in keeping with the Field of Interest of the IEEE Computational Intelligence Society (IEEE/CIS). 

Full Aims & Scope